Computed Tomography (CT) data depicting internal patient structures may be used for diagnosis, dose planning and patient positioning. Acquisition of CT data is time-consuming and exposes a patient to potentially harmful radiation. Accordingly, some conventional systems obtain surface data using a surface-scanning camera, identify anatomical landmarks based on the surface data, and position a patient based on the landmarks. Such positioning is not as accurate as positioning based on CT data, and these conventional systems do not alleviate the need for CT data in diagnosis or dose planning.
It has been considered to utilize neural networks to generate CT data based on skin surface data, formulated as a per-pixel classification or regression. These formulations treat each output pixel as conditionally-independent and therefore fail to capture structure information in the output space. What is needed is a network design and training architecture which provides suitable CT data from skin surface data.